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arXiv 2307.04040cs.RO
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基于计划集成的元策略学习用于鲁棒的铰接物体操作

Meta-Policy Learning over Plan Ensembles for Robust Articulated Object Manipulation

Rice University（莱斯大学）；NVIDIA Research（英伟达研究院）

Constantinos Chamzas, Caelan Garrett, Balakumar Sundaralingam, Lydia E. Kavraki, Dieter Fox

备注：
5 pages, Workshop on Learning for Task and Motion Planning (RSS2023)
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AI总结：

本文提出利用几何模型驱动规划器构建路径策略混合体，并学习任务特定元策略，在7自由度Franka-Emika机器人推门任务中实现少数据、抗模型不确定性的鲁棒操作。

摘要：

近期研究表明，诸如推或倒等复杂操作技能可以通过最先进的基于学习的技术（如强化学习（RL））来学习。然而，这些方法通常样本复杂度高，容易受到领域变化的影响，并且会产生机器人不应执行的不安全动作。另一方面，纯几何模型驱动的规划可以生成满足机器人所有几何约束的复杂行为，但对于给定环境可能不具备动力学可行性。在本工作中，我们利用几何模型驱动的规划器构建一组路径策略的混合体，并在此基础上学习一个任务特定的元策略以完成任务。实验结果表明，可以学习到一个成功的元策略来推门，同时所需数据很少，并且对环境模型不确定性具有鲁棒性。我们在仿真中使用一台7自由度Franka-Emika机器人推柜门来测试我们的方法。

英文摘要：

Recent work has shown that complex manipulation skills, such as pushing or pouring, can be learned through state-of-the-art learning based techniques, such as Reinforcement Learning (RL). However, these methods often have high sample-complexity, are susceptible to domain changes, and produce unsafe motions that a robot should not perform. On the other hand, purely geometric model-based planning can produce complex behaviors that satisfy all the geometric constraints of the robot but might not be dynamically feasible for a given environment. In this work, we leverage a geometric model-based planner to build a mixture of path-policies on which a task-specific meta-policy can be learned to complete the task. In our results, we demonstrate that a successful meta-policy can be learned to push a door, while requiring little data and being robust to model uncertainty of the environment. We tested our method on a 7-DOF Franka-Emika Robot pushing a cabinet door in simulation.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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